A functional cellular framework for sex and estrous cycle-dependent gene expression and behavior.

Sex hormones exert a profound influence on gendered behaviors. How individual sex hormone-responsive neuronal populations regulate diverse sex-typical behaviors is unclear. We performed orthogonal, genetically targeted sequencing of four estrogen receptor 1-expressing (Esr1+) populations and identified 1,415 genes expressed differentially between sexes or estrous states. Unique subsets of these genes were distributed across all 137 transcriptomically defined Esr1+ cell types, including estrous stage-specific ones, that comprise the four populations. We used differentially expressed genes labeling single Esr1+ cell types as entry points to functionally characterize two such cell types, BNSTprTac1/Esr1 and VMHvlCckar/Esr1. We observed that these two cell types, but not the other Esr1+ cell types in these populations, are essential for sex recognition in males and mating in females, respectively. Furthermore, VMHvlCckar/Esr1 cell type projections are distinct from those of other VMHvlEsr1 cell types. Together, projection and functional specialization of dimorphic cell types enables sex hormone-responsive populations to regulate diverse social behaviors.

Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images.

Spatial transcriptomics techniques such as STARmap [15] enable the subcellular detection of RNA transcripts within complex tissue sections. The data from these techniques are impacted by optical microscopy limitations, such as shading or vignetting effects from uneven illumination during image capture. Downstream analysis of these sparse spatially resolved transcripts is dependent upon the correction of these artefacts. This paper introduces a novel non-parametric vignetting correction tool for spatial transcriptomic images, which estimates the illumination field and background using an efficient iterative sliced histogram normalization routine. We show that our method outperforms the state-of-the-art shading correction techniques both in terms of illumination and background field estimation and requires fewer input images to perform the estimation adequately. We further demonstrate an important downstream application of our technique, showing that spatial transcriptomic volumes corrected by our method yield a higher and more uniform gene expression spot-calling in the rodent hippocampus. Python code and a demo file to reproduce our results are provided in the supplementary material and at this github page: https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st.

Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development.

Transcriptional programs control cellular lineage commitment and differentiation during development. Understanding of cell fate has been advanced by studying single-cell RNA-sequencing (RNA-seq) but is limited by the assumptions of current analytic methods regarding the structure of data. We present single-cell topological data analysis (scTDA), an algorithm for topology-based computational analyses to study temporal, unbiased transcriptional regulation. Unlike other methods, scTDA is a nonlinear, model-independent, unsupervised statistical framework that can characterize transient cellular states. We applied scTDA to the analysis of murine embryonic stem cell (mESC) differentiation in vitro in response to inducers of motor neuron differentiation. scTDA resolved asynchrony and continuity in cellular identity over time and identified four transient states (pluripotent, precursor, progenitor, and fully differentiated cells) based on changes in stage-dependent combinations of transcription factors, RNA-binding proteins, and long noncoding RNAs (lncRNAs). scTDA can be applied to study asynchronous cellular responses to either developmental cues or environmental perturbations.

Scalable microfluidics for single-cell RNA printing and sequencing.

Many important biological questions demand single-cell transcriptomics on a large scale. Hence, new tools are urgently needed for efficient, inexpensive manipulation of RNA from individual cells. We report a simple platform for trapping single-cell lysates in sealed, picoliter microwells capable of printing RNA on glass or capturing RNA on beads. We then develop a scalable technology for genome-wide, single-cell RNA-Seq. Our device generates pooled libraries from hundreds of individual cells with consumable costs of $0.10-$0.20 per cell and includes five lanes for simultaneous experiments. We anticipate that this system will serve as a general platform for single-cell imaging and sequencing.

Positive feedback regulates switching of phosphate transporters in S. cerevisiae.

The regulation of transporters by nutrient-responsive signaling pathways allows cells to tailor nutrient uptake to environmental conditions. We investigated the role of feedback generated by transporter regulation in the budding yeast phosphate-responsive signal transduction (PHO) pathway. Cells starved for phosphate activate feedback loops that regulate high- and low-affinity phosphate transport. We determined that positive feedback is generated by PHO pathway-dependent upregulation of Spl2, a negative regulator of low-affinity phosphate uptake. The interplay of positive and negative feedback loops leads to bistability in phosphate transporter usage--individual cells express predominantly either low- or high-affinity transporters, both of which can yield similar phosphate uptake capacity. Cells lacking the high-affinity transporter, and associated negative feedback, exhibit phenotypes that arise from hysteresis due to unopposed positive feedback. In wild-type cells, population heterogeneity generated by feedback loops may provide a strategy for anticipating changes in environmental phosphate levels.